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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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and advanced material design and fabrication. Through this multidisciplinary project, the student will develop expertise in: Hands-on experience with advanced computational physics and materials
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. The high prevalence of iron deficiency in athletes is likely due to a combination of inadequate iron intake (low energy intakes and vegetarian/vegan diets) and increased iron losses associated with physical
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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defined by Swansea University) in Engineering, physical sciences or similar relevant science discipline. We also welcome applications from graduates in computational science or mathematics. Note for
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to the launch of the Bloomberg Cambridge University Corporate Bond Index later in 2025 and the delivery of the ongoing research programme related to the index project. The successful candidate will undertake desk
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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candidates with: • Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) • Willingness to adapt and work across different